Setup

Load packages

library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
library(scales) # scales on plots
library(lme4) # for linear mixed models

R / package versions used

R.Version() 
## $platform
## [1] "x86_64-w64-mingw32"
## 
## $arch
## [1] "x86_64"
## 
## $os
## [1] "mingw32"
## 
## $system
## [1] "x86_64, mingw32"
## 
## $status
## [1] ""
## 
## $major
## [1] "4"
## 
## $minor
## [1] "0.5"
## 
## $year
## [1] "2021"
## 
## $month
## [1] "03"
## 
## $day
## [1] "31"
## 
## $`svn rev`
## [1] "80133"
## 
## $language
## [1] "R"
## 
## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
## 
## $nickname
## [1] "Shake and Throw"
packageVersion('here')
## [1] '1.0.1'
packageVersion('tidyverse')
## [1] '1.3.1'
packageVersion('scales') 
## [1] '1.1.1'
packageVersion('lme4') 
## [1] '1.1.27.1'

Read pre-wrangled data

(see 00-wrangling-setup.Rmd script)

# here::here()
apt <- readRDS(here("data", "apt-data.rds"))
apt <- as_tibble(apt)
head(apt)
## # A tibble: 6 x 48
##   session    group ppt   selection selected continuation continue L1_bsl
##   <fct>      <fct> <fct> <fct>        <dbl> <fct>           <dbl>  <dbl>
## 1 pre-degree pilot HW002 rejected         0 na                  0      0
## 2 pre-degree pilot HW004 rejected         0 na                  0      0
## 3 pre-degree pilot HW009 rejected         0 na                  0      0
## 4 pre-degree pilot HW011 rejected         0 na                  0      0
## 5 pre-degree pilot HW012 rejected         0 na                  0      0
## 6 pre-degree pilot HW014 rejected         0 na                  0      0
## # ... with 40 more variables: self_rating <dbl>, bsl_years <chr>, age_s1 <dbl>,
## #   nback_lett <dbl>, nback_spat <dbl>, nback_comb <dbl>, corsi_bspan <dbl>,
## #   corsi_score <dbl>, corsi_corr <dbl>, corsi_mspan <dbl>, kirk_ceil <dbl>,
## #   kirk_raw <dbl>, kirk_acc <dbl>, kbit_ceil <dbl>, kbit_raw <dbl>,
## #   kbit_acc <dbl>, dspan_mem <dbl>, dspan_corr <dbl>, dspan_time <dbl>,
## #   mr2d_acc <dbl>, mr2d_rt <dbl>, mr2d_sats <dbl>, mr3d_acc <dbl>,
## #   mr3d_rt <dbl>, mr3d_sats <dbl>, bis_tot <dbl>, bis_att <dbl>, ...

Filter out participants who did not progress beyond interview

apt <- apt %>% filter(selection == "selected")

Filter out participants with deaf family members / L1 BSL

apt <- apt %>% filter(L1_bsl != 1)

Convert data from long format to wide format

apt_wide <- apt %>% 
  select(-comments) %>% 
  tidyr::pivot_wider(names_from = session,
                     values_from = c(nback_lett:grade_terp))

Plots & correlations

# set up a theme for the plots
theme_details <- theme_bw() + 
    theme(panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank(),
          panel.border = element_rect(colour = "black", size=0.5),
          plot.title = element_text(face = "bold"),
          axis.title.x = element_text(face = "bold"),
          axis.title.y = element_text(face = "bold"),
          axis.text.x = element_text(size=10, colour="black"),
          axis.text.y = element_text(colour="black"),
          legend.position = "none")

BSL: predicted impact

Corsi Blocks

vs BSL Grades
# Initial visuospatial skill vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Initial visuospatial skill vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000657
# Initial visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial visuospatial skill vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000256
# 1st year visuospatial skill vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "1st year visuospatial skill vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "1st year Corsi Blocks score ")

apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.153
# 1st year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "1st year visuospatial skill vs. 2nd year BSL grades", 
       y = "1st year BSL grade", 
       x = "2nd year Corsi Blocks score ")

apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0867
# 2nd year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(7, 10)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year visuospatial skill vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "2nd year Corsi Blocks score ")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00871
vs BSL-SRT

Does Corsi Blocks performance relate to BSL Sentence Reproduction Task?

# Initial Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  coord_cartesian(xlim = c(6, 10)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial Corsi Blocks score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.101
# 2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "2nd year Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.609
corsi2_srt3 <- lm(`bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating,
                  data = apt_wide)
summary(corsi2_srt3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
##  0.50226 -1.33521 -1.85779 -3.44244 -0.01693  3.98307  2.39842 -0.23138 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -6.3634     7.0008  -0.909   0.4051  
## `corsi_corr_2nd year`   2.1591     0.7907   2.731   0.0412 *
## self_rating             0.1072     0.9523   0.113   0.9147  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.793 on 5 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.6099, Adjusted R-squared:  0.4539 
## F-statistic: 3.909 on 2 and 5 DF,  p-value: 0.09503
# 3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.301
corsi3_srt3 <- lm(`bsl_srt_3rd year` ~ `corsi_corr_3rd year` +
                    self_rating, data = apt_wide)
summary(corsi3_srt3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2436 -2.3397  0.2179  1.3910  5.9103 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -1.9487     9.3428  -0.209    0.842
## `corsi_corr_3rd year`   1.6346     1.0570   1.547    0.173
## self_rating            -0.1538     1.1578  -0.133    0.899
## 
## Residual standard error: 3.409 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.3029, Adjusted R-squared:  0.07051 
## F-statistic: 1.303 on 2 and 6 DF,  p-value: 0.3388

2D Mental Rotation

vs BSL Grades

Does 2D Mental Rotation performance relate to BSL grades?

# Initial 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "Initial 2D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00148
# Initial 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00985
# 1st year 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "1st year 2D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "1st year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.482
# 1st year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "1st year 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "1st year BSL grade", 
       x = "2nd year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.121
# 2nd year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  coord_cartesian(xlim = c(-1.5, 1.5)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "2nd year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.265
mr2d2_bsl2 <- lm(`grade_bsl_2nd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide)
summary(mr2d2_bsl2)
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.457  -7.042   2.662   5.844   7.150 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            59.592      5.783  10.305 1.75e-05 ***
## `mr2d_sats_2nd year`    4.414      2.687   1.643    0.144    
## self_rating             1.657      2.465   0.672    0.523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.139 on 7 degrees of freedom
##   (26 observations deleted due to missingness)
## Multiple R-squared:  0.3095, Adjusted R-squared:  0.1122 
## F-statistic: 1.569 on 2 and 7 DF,  p-value: 0.2736
vs BSL-SRT

Does 2D Mental Rotation performance relate to BSL Sentence Reproduction Task?

# 2nd year 2D Mental Rotation score vs. 2nd year BSL-SRT scores
# just 4 data points here

# 3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.191
mr2d3_srt3 <- lm(`bsl_srt_3rd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide)
summary(mr2d3_srt3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3848 -0.7349  1.0126  1.5772  3.8567 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             9.818      2.856   3.438   0.0138 *
## `mr2d_sats_3rd year`    3.092      2.061   1.500   0.1842  
## self_rating             1.056      1.149   0.920   0.3932  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.438 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.291,  Adjusted R-squared:  0.05465 
## F-statistic: 1.231 on 2 and 6 DF,  p-value: 0.3564

3D Mental Rotation

vs BSL Grades

Does 3D Mental Rotation performance relate to BSL grades?

# Initial 3D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "Initial 3D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0671
mr3d0_bsl1 <- lm(`grade_bsl_1st year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide)
summary(mr3d0_bsl1)
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mr3d_sats_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.040  -5.622  -1.414   7.158  18.157 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             65.4722     5.8011  11.286 2.56e-09 ***
## `mr3d_sats_pre-degree`   1.5614     1.3900   1.123    0.277    
## self_rating             -0.4048     2.1124  -0.192    0.850    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.884 on 17 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0691, Adjusted R-squared:  -0.04042 
## F-statistic: 0.6309 on 2 and 17 DF,  p-value: 0.5441
# Initial 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial 3D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000282
# 2nd year 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "2nd year 3D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "2nd year 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.386
mr3d2_bsl2 <- lm(`grade_bsl_2nd year` ~ `mr3d_sats_2nd year` +
                   self_rating, data = apt_wide)
summary(mr3d2_bsl2)
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr3d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.5850 -5.8497  0.7585  3.7502  9.7940 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           61.8296     4.9508  12.489 5.48e-07 ***
## `mr3d_sats_2nd year`   4.6447     2.0988   2.213   0.0542 .  
## self_rating           -0.7094     2.3738  -0.299   0.7719    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.986 on 9 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.3924, Adjusted R-squared:  0.2574 
## F-statistic: 2.906 on 2 and 9 DF,  p-value: 0.1062
vs BSL-SRT

Does 3D Mental Rotation performance relate to BSL Sentence Reproduction Task?

# Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-3, 1.5)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.369
mr3d0_srt3 <- lm(`bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide)
summary(mr3d0_srt3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8469 -0.7479 -0.2005  1.9028  3.2185 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            14.55018    2.71305   5.363  0.00172 **
## `mr3d_sats_pre-degree`  1.90099    1.05072   1.809  0.12040   
## self_rating            -0.02618    1.06583  -0.025  0.98120   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.243 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.3692, Adjusted R-squared:  0.1589 
## F-statistic: 1.756 on 2 and 6 DF,  p-value: 0.2511
# 2nd year 3D Mental Rotation score vs. 2nd year BSL-SRT scores
# only 4 data points here

# 3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.123

MLAT Number Learning

vs BSL Grades
# Initial MLAT Number Learning Accuracy vs. BSL grades 1st year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`grade_bsl_1st year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 1st year", 
       x = "MLAT Number Learning Accuracy", y = "BSL grade 1st year")

apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0215
mlat_bsl_1 <- lm(`grade_bsl_1st year` ~ `mlat_acc_pre-degree` +
                   self_rating, data = apt_wide)
summary(mlat_bsl_1)
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mlat_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.9838  -7.0550  -0.3413   7.5274  14.1245 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            54.7371    13.3127   4.112 0.000728 ***
## `mlat_acc_pre-degree`   8.4969    12.4720   0.681 0.504872    
## self_rating             0.7031     2.3654   0.297 0.769878    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.11 on 17 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02658,    Adjusted R-squared:  -0.08794 
## F-statistic: 0.2321 on 2 and 17 DF,  p-value: 0.7954
# Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`grade_bsl_2nd year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year", 
       x = "MLAT Number Learning Accuracy", y = "BSL grade 2nd year")

apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0333
mlat_bsl_2 <- lm(`grade_bsl_2nd year` ~ `mlat_acc_pre-degree` +
                   self_rating, data = apt_wide)
summary(mlat_bsl_2)
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mlat_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.216  -7.027   3.224   7.449  17.760 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             49.048     16.575   2.959   0.0104 *
## `mlat_acc_pre-degree`   13.098     15.646   0.837   0.4166  
## self_rating              1.405      2.991   0.470   0.6459  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.33 on 14 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04827,    Adjusted R-squared:  -0.08769 
## F-statistic: 0.355 on 2 and 14 DF,  p-value: 0.7073
vs BSL-SRT
# Initial MLAT Number Learning Accuracy vs. BSL-SRT 3rd year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`bsl_srt_3rd year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL-SRT score 3rd year", 
       x = "MLAT Number Learning Accuracy")

apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000354

BSL: no predicted impact

Kirklees Sentence Reading

vs BSL Grades
# Kirklees Sentence Reading pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(aes(color = continuation)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.188
kirk0_bsl1 <- lm(`grade_bsl_1st year` ~ `kirk_acc_pre-degree` +
                   self_rating, data = apt_wide)
summary(kirk0_bsl1)
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `kirk_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.5382  -4.7901   0.6237   3.9859  13.0213 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             32.187     11.757   2.738   0.0110 *
## `kirk_acc_pre-degree`   35.345     13.333   2.651   0.0135 *
## self_rating              1.318      1.356   0.972   0.3402  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.713 on 26 degrees of freedom
##   (7 observations deleted due to missingness)
## Multiple R-squared:  0.2161, Adjusted R-squared:  0.1558 
## F-statistic: 3.583 on 2 and 26 DF,  p-value: 0.04223
# Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.55, .95)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0189
# Kirklees Sentence Reading 1st year vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "Kirklees Sentence Reading after 1 year")

apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0417
# Kirklees Sentence Reading 1st year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading after 1 year")

apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0853
# Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.6, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00824
vs BSL-SRT

Does Kirklees Sentence Reading performance relate to BSL Sentence Reproduction Task?

# Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.55, .95)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial Kirklees Sentence Reading score")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.237
kirk0_srt3 <- lm(`bsl_srt_3rd year` ~ `kirk_acc_pre-degree` +
                   self_rating, data = apt_wide)
summary(kirk0_srt3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `kirk_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1089 -1.8031 -0.1845  2.5358  4.6470 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -2.1142     9.3522  -0.226    0.829
## `kirk_acc_pre-degree`  16.9416    10.8402   1.563    0.169
## self_rating             0.8558     1.0985   0.779    0.466
## 
## Residual standard error: 3.398 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.3071, Adjusted R-squared:  0.0761 
## F-statistic: 1.329 on 2 and 6 DF,  p-value: 0.3327
# 2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year Kirklees Sentence Reading score")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.149

KBIT-2 Matrices

vs BSL Grades
# KBIT-2 Matrices pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0115
# KBIT-2 Matrices pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0134
# KBIT-2 Matrices 1st year vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "KBIT-2 Matrices 1st year")

apt_wide %>% 
  filter(`kbit_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0273
# KBIT-2 Matrices 1st year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices 1st year")

apt_wide %>% 
  filter(`kbit_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000593
# KBIT-2 Matrices 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.167
vs BSL-SRT

Does KBIT-2 Matrices performance relate to BSL Sentence Reproduction Task?

# Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.84, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial KBIT-2 Matrices score")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00494
# 2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year KBIT-2 Matrices score")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0933

Dual N-Back

vs BSL Grades
# Dual N-Back pre-degree vs. BSL Grades 1st year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. BSL Grades 1st year", 
       y = "1st year BSL grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0190
# Dual N-Back pre-degree vs. BSL Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. BSL Grades 2nd year", 
       y = "2nd year BSL grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0101
# Dual N-Back 2nd year vs. BSL Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. BSL Grades 2nd year", 
       y = "2nd year BSL grade", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0520
vs BSL-SRT

Does Dual N-Back performance relate to BSL Sentence Reproduction Task?

# Initial Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial Dual N-Back score")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00186
# 2nd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "2nd year Dual N-Back score")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.218
# 3rd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "3rd year Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year Dual N-Back score")

apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.122
nback3_srt3 <- lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` +
                   self_rating, data = apt_wide)
summary(nback3_srt3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3928 -0.6371 -0.3928  0.6072  6.9485 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)           -19.9281    38.7294  -0.515    0.625
## `nback_comb_3rd year`  46.3473    56.0801   0.826    0.440
## self_rating            -0.1952     1.4719  -0.133    0.899
## 
## Residual standard error: 3.82 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.1246, Adjusted R-squared:  -0.1671 
## F-statistic: 0.4272 on 2 and 6 DF,  p-value: 0.6707

Digit Span

vs BSL Grades
# Digit Span pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.122
# Digit Span pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.141
# No datapoints for Digit Span @ '1 year of study'

# Digit Span 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Digit Span 2nd year")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0796
vs BSL-SRT

Does Digit Span score relate to BSL-SRT score?

# Initial Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial Digit Span score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial Digit Span score")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00124
# 2nd year Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year Digit Span score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year Digit Span score")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00433

Barratt Impulsiveness Scale

vs BSL Grades
# Barratt Impulsiveness Scale 2nd year vs BSL grades 1st year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_bsl_1st year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "BIS 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0468
# Barratt Impulsiveness Scale 2nd year vs BSL grades 2nd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_bsl_2nd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "BIS 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00858
vs BSL-SRT
# Barratt Impulsiveness Scale 2nd year vs BSL-SRT 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`bsl_srt_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. BSL-SRT 3rd year", 
       y = "3rd year BSL-SRT score", x = "BIS Score 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.272
bis2_srt3 <- lm(`bsl_srt_3rd year` ~ `bis_tot_2nd year` +
                   self_rating, data = apt_wide)
summary(bis2_srt3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `bis_tot_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7743 -1.2297  0.4997  2.5887  4.0756 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         25.0529     9.3110   2.691    0.036 *
## `bis_tot_2nd year`  -0.1815     0.1248  -1.454    0.196  
## self_rating         -0.2987     1.2260  -0.244    0.816  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.466 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.2791, Adjusted R-squared:  0.03883 
## F-statistic: 1.162 on 2 and 6 DF,  p-value: 0.3746

Interpreting: predicted impact

Dual N-Back

vs Interpreting Grades
# Dual N-Back pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0123
# Dual N-Back pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.114
# Dual N-Back 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0343
vs Eng>BSL interpreting
# Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.125
# Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.121
# Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 3rd year")

apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.155
nback3_e2b3 <- lm(`terp_e2b_3rd year` ~ `nback_comb_3rd year` +
                   self_rating, data = apt_wide)
summary(nback3_e2b3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.3649  -5.7844  -1.9770   0.6793  20.5151 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -38.624    133.735  -0.289    0.782
## `nback_comb_3rd year`  136.419    193.649   0.704    0.508
## self_rating              1.444      5.083   0.284    0.786
## 
## Residual standard error: 13.19 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.1663, Adjusted R-squared:  -0.1117 
## F-statistic: 0.5982 on 2 and 6 DF,  p-value: 0.5796
vs BSL>Eng interpreting
# Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0605
# Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.178
# Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 3rd year")

apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.103

Corsi Blocks

vs Interpreting Grades
# Corsi Blocks pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(8, 10)) +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0114
# Corsi Blocks pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00830
# Corsi Blocks 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks 1st year")

apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.164
# Corsi Blocks 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks 2nd year")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00315
vs Eng>BSL interpreting
#  Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(6, 10)) +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.289
corsi0_e2b3 <- lm(`terp_e2b_3rd year` ~ `corsi_corr_pre-degree` +
                   self_rating, data = apt_wide)
summary(corsi0_e2b3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.9254  -8.4322  -0.1853   1.9101  22.9235 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)             19.58284   29.41334   0.666     0.53
## `corsi_corr_pre-degree`  5.07783    3.98831   1.273     0.25
## self_rating             -0.06448    4.75634  -0.014     0.99
## 
## Residual standard error: 12.18 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.2893, Adjusted R-squared:  0.0524 
## F-statistic: 1.221 on 2 and 6 DF,  p-value: 0.359
#  Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 2nd year")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.565
corsi2_e2b3 <- lm(`terp_e2b_3rd year` ~ `corsi_corr_2nd year` +
                   self_rating, data = apt_wide)
summary(corsi2_e2b3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     13     15     17     18     20     21     22     26 
## -8.536 -1.520  2.139 -6.065 -9.681  3.519  8.615 11.529 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             1.3786    23.2375   0.059   0.9550  
## `corsi_corr_2nd year`   6.5196     2.6245   2.484   0.0556 .
## self_rating             0.6451     3.1611   0.204   0.8463  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.271 on 5 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.5687, Adjusted R-squared:  0.3962 
## F-statistic: 3.297 on 2 and 5 DF,  p-value: 0.1222
#  Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 3rd year")

apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.132
vs BSL>Eng interpreting
#  Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(6, 10)) +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0578
#  Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 2nd year")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.455
corsi2_b2e3 <- lm(`terp_b2e_3rd year` ~ `corsi_corr_2nd year` +
                   self_rating, data = apt_wide)
summary(corsi2_b2e3)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `corsi_corr_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22 
## -0.3476 -0.3357  4.8847 -2.7752 -3.9221 -1.3221  3.8180 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            45.2283    10.4934   4.310   0.0125 *
## `corsi_corr_2nd year`   2.2068     1.2131   1.819   0.1430  
## self_rating            -0.3604     1.5418  -0.234   0.8266  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.984 on 4 degrees of freedom
##   (29 observations deleted due to missingness)
## Multiple R-squared:  0.4624, Adjusted R-squared:  0.1937 
## F-statistic: 1.721 on 2 and 4 DF,  p-value: 0.289
#  Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 3rd year")

apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0169

Digit Span

vs Interpreting Grades
# Digit Span pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.6, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0155
# Digit Span pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.237
dspan0_sli2 <- lm(`grade_terp_2nd year` ~ `dspan_corr_pre-degree` +
                   self_rating, data = apt_wide)
summary(dspan0_sli2)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.356  -4.900   2.872   4.838  16.355 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               21.078     20.770   1.015   0.3320  
## `dspan_corr_pre-degree`   51.655     26.355   1.960   0.0758 .
## self_rating                1.479      2.344   0.631   0.5410  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.655 on 11 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2638, Adjusted R-squared:  0.1299 
## F-statistic: 1.971 on 2 and 11 DF,  p-value: 0.1856
# Digit Span 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0207
vs Eng>BSL interpreting
# Digit Span pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0926
# Digit Span 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span 2nd year")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0851
# Digit Span 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.7, .86)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span 3rd year")

apt_wide %>% 
  filter(`dspan_corr_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.115
vs BSL>Eng interpreting
# Digit Span pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .72)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00697
# Digit Span 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span 2nd year")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0863
# Digit Span 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span 3rd year")

apt_wide %>% 
  filter(`dspan_corr_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0264

Summarising Task

vs Interpreting Grades
# Summarising pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Summarising pre-degree")

apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00243
vs Eng>BSL interpreting
# Summarising pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. English to BSL interpreting 3rd year", 
       y = "English to BSL interpreting 3rd year", x = "Summarising pre-degree")

apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0447
vs BSL>Eng interpreting
# Summarising pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. BSL to English interpreting 3rd year", 
       y = "BSL to English interpreting 3rd year", x = "Summarising pre-degree")

apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00303

Interpreting: no predicted impact

Kirklees Sentence Reading

vs Interpreting Grades
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.48, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.160
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.56, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.133
# Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading after 1 year")

apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0201
# Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0690
vs Eng>BSL interpreting
# Kirklees pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.56, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Kirklees pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0754
# Kirklees 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Kirklees 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0193
vs BSL>Eng interpreting
# Kirklees pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.56, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Kirklees pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0922
# Kirklees 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Kirklees 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000130

KBIT-2 Matrices

vs Interpreting Grades
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.82, .95)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0419
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0222
# KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 1st year")

apt_wide %>% 
  filter(`kbit_acc_1st year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00117
# KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0234
vs Eng>BSL interpreting
# KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.84, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000458
# KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000597
vs BSL>Eng interpreting
# KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.84, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0417
# KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0172

2D Mental Rotation

vs Interpreting Grades
# 2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "2D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0428
# 2D Mental Rotation 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-1.5, 1.5)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "2D Mental Rotation 1st year")

apt_wide %>% 
  filter(`mr2d_sats_2nd year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.242
mr2d2_sli2 <- lm(`grade_terp_2nd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide)
summary(mr2d2_sli2)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.284  -4.399  -1.949   7.699  11.265 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            57.147      6.889   8.296 3.36e-05 ***
## `mr2d_sats_2nd year`    4.883      2.986   1.635    0.141    
## self_rating             1.538      2.966   0.519    0.618    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.793 on 8 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2671, Adjusted R-squared:  0.08387 
## F-statistic: 1.458 on 2 and 8 DF,  p-value: 0.2885
# 2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-.5, 1.3)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 3rd year vs. Interpreting Grades 2nd year", 
       y = "3rd year Interpreting grade", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.388
mr2d3_sli2 <- lm(`grade_terp_2nd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide)
summary(mr2d3_sli2)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.9202  -0.3733   2.2721   3.5377   5.8433 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            47.717      5.755   8.291 0.000167 ***
## `mr2d_sats_3rd year`   12.084      4.154   2.909 0.027005 *  
## self_rating             4.274      2.315   1.847 0.114322    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.927 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:   0.61,  Adjusted R-squared:  0.4799 
## F-statistic: 4.692 on 2 and 6 DF,  p-value: 0.05934
vs Eng>BSL interpreting
# 2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.198
mr2d2_e2b3 <- lm(`terp_e2b_3rd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide)
summary(mr2d2_e2b3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
## -12.4180   3.6451 -13.5885   8.6608  -5.9722  -0.5928  11.9377   8.3277 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            54.281      8.546   6.352  0.00143 **
## `mr2d_sats_2nd year`    6.600      4.541   1.453  0.20586   
## self_rating             3.945      4.118   0.958  0.38211   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.62 on 5 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.3226, Adjusted R-squared:  0.05164 
## F-statistic: 1.191 on 2 and 5 DF,  p-value: 0.3777
# 2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 3rd year")

apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.180
mr2d3_e2b3 <- lm(`terp_e2b_3rd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide)
summary(mr2d3_e2b3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.6541  -6.8081   0.8105   8.6950  10.6821 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            47.042      9.252   5.084  0.00226 **
## `mr2d_sats_3rd year`   11.777      6.678   1.764  0.12825   
## self_rating             5.609      3.721   1.507  0.18246   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.14 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.4055, Adjusted R-squared:  0.2073 
## F-statistic: 2.046 on 2 and 6 DF,  p-value: 0.2101
vs BSL>Eng interpreting
# 2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.603
mr2d2_b2e3 <- lm(`terp_b2e_3rd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide)
summary(mr2d2_b2e3)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22 
## -2.1578  0.6943 -1.4392  3.7661 -0.9345 -2.6363  2.7073 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            62.597      2.242  27.920 9.79e-06 ***
## `mr2d_sats_2nd year`    3.630      1.231   2.950    0.042 *  
## self_rating             1.202      1.132   1.062    0.348    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.023 on 4 degrees of freedom
##   (29 observations deleted due to missingness)
## Multiple R-squared:  0.6906, Adjusted R-squared:  0.536 
## F-statistic: 4.465 on 2 and 4 DF,  p-value: 0.0957
# 2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.311
mr2d3_b2e3 <- lm(`terp_b2e_3rd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide)
summary(mr2d3_b2e3)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
## -3.0757  2.8391 -0.2476  2.5267 -1.6851 -5.1391  2.8633  1.9187 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            59.962      3.023  19.834 6.02e-06 ***
## `mr2d_sats_3rd year`    4.575      2.164   2.114   0.0881 .  
## self_rating             1.771      1.275   1.389   0.2235    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.608 on 5 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.5029, Adjusted R-squared:  0.3041 
## F-statistic:  2.53 on 2 and 5 DF,  p-value: 0.1742

3D Mental Rotation

vs Interpreting Grades
# 3D Mental Rotation pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-2, 1.5)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "3D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0677
# 3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title= "3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "3D Mental Rotation pre-degree") 

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.244
# 3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title= "3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
       y = "2nd year Interpreting grade", x = "3D Mental Rotation 2nd year") 

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.314
# 3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title= "3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
       y = "2nd year Interpreting grade", x = "3D Mental Rotation 3rd year") 

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.542
mr3d3_sli2 <- lm(`grade_terp_2nd year` ~ `mr3d_sats_3rd year` +
                   self_rating, data = apt_wide)
summary(mr3d3_sli2)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr3d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.8513  -3.6800  -0.8694   5.5280   8.2989 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            49.137      5.605   8.766 0.000122 ***
## `mr3d_sats_3rd year`    7.181      2.524   2.845 0.029386 *  
## self_rating             2.064      2.219   0.930 0.388267    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.018 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.5997, Adjusted R-squared:  0.4662 
## F-statistic: 4.494 on 2 and 6 DF,  p-value: 0.06416
vs Eng>BSL interpreting
# 3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-2.7, 1.2)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.268
mr3d0_e2b3 <- lm(`terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide)
summary(mr3d0_e2b3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.243  -5.679   1.404   2.243  18.096 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              62.273     10.114   6.157 0.000842 ***
## `mr3d_sats_pre-degree`    5.158      3.917   1.317 0.235955    
## self_rating               2.062      3.973   0.519 0.622317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.09 on 6 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.2997, Adjusted R-squared:  0.06625 
## F-statistic: 1.284 on 2 and 6 DF,  p-value: 0.3435
# 3D Mental Rotation 2nd year vs. Eng to BSL interpreting  3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-1.2, 2.2)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0974
# 3D Mental Rotation 3rd year vs. Eng to BSL interpreting  3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 3rd year")

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00752
vs BSL>Eng interpreting
# 3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-2.7, 1.2)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.475
mr3d0_b2e3 <- lm(`terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide)
summary(mr3d0_b2e3)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
## -4.4451  0.3171  0.6291 -0.3183 -1.4933 -2.9145  2.7963  5.4287 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             66.4684     3.1264  21.260 4.27e-06 ***
## `mr3d_sats_pre-degree`   2.4042     1.1997   2.004    0.101    
## self_rating              0.2235     1.2903   0.173    0.869    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.698 on 5 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.4779, Adjusted R-squared:  0.2691 
## F-statistic: 2.289 on 2 and 5 DF,  p-value: 0.1969
# 3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-1.2, 2.2)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.286
# 3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.158

Barratt Impulsiveness Scale

vs Interpreting Grades
# Barratt Impulsiveness Scale 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_terp_2nd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "BIS score 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0431
vs Eng>BSL interpreting
# Barratt Impulsiveness Scale 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`terp_e2b_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. Eng to BSL interpreting 3rd year", 
       y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.115
vs BSL>Eng interpreting
# Barratt Impulsiveness Scale 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`terp_b2e_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. BSL to Eng interpreting 3rd year", 
       y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.236

Models

BSL Grades

What best predicts BSL grades?

( bsl_grade_mdl <- lm(grade_bsl ~ nback_comb +
                                  mr2d_sats +
                                  mr3d_sats + 
                                  corsi_corr +
                                  kirk_acc +
                                  self_rating, 
                                  data = apt) ) 
## 
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats + 
##     corsi_corr + kirk_acc + self_rating, data = apt)
## 
## Coefficients:
## (Intercept)   nback_comb    mr2d_sats    mr3d_sats   corsi_corr     kirk_acc  
##     196.801     -198.704       -2.099        7.461       -1.192       28.097  
## self_rating  
##      -1.650
summary(bsl_grade_mdl)
## 
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats + 
##     corsi_corr + kirk_acc + self_rating, data = apt)
## 
## Residuals:
##      66      69      70      73      75      77      78      80      81      86 
##  9.5971 -6.8402 -2.0906  3.0107  2.9968 -4.1287  0.2737 -5.8599  3.5617 -0.5206 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  196.801    138.493   1.421    0.250
## nback_comb  -198.704    166.257  -1.195    0.318
## mr2d_sats     -2.099      4.699  -0.447    0.685
## mr3d_sats      7.461      4.335   1.721    0.184
## corsi_corr    -1.192      3.203  -0.372    0.735
## kirk_acc      28.097     33.411   0.841    0.462
## self_rating   -1.650      3.484  -0.474    0.668
## 
## Residual standard error: 8.674 on 3 degrees of freedom
##   (110 observations deleted due to missingness)
## Multiple R-squared:  0.6639, Adjusted R-squared:  -0.008299 
## F-statistic: 0.9877 on 6 and 3 DF,  p-value: 0.5499
# only fit on 10 observations

# wide version - pre-degree
( bsl_grade_mdl_2 <- lm(`grade_bsl_2nd year` ~ 
                                  `nback_comb_pre-degree` +
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `kirk_acc_pre-degree` + 
                                  `mlat_acc_pre-degree` +
                                  self_rating,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `mlat_acc_pre-degree` + self_rating, data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                 -7.0127                  36.4042                  -0.8714  
## `corsi_corr_pre-degree`    `kirk_acc_pre-degree`    `mlat_acc_pre-degree`  
##                  0.8696                  26.7125                  10.5758  
##             self_rating  
##                  2.3496
summary(bsl_grade_mdl_2)
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `mlat_acc_pre-degree` + self_rating, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.834  -2.578   3.819   5.891  15.540 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -7.0127    84.6677  -0.083    0.936
## `nback_comb_pre-degree`  36.4042    93.6533   0.389    0.706
## `mr3d_sats_pre-degree`   -0.8714     2.4931  -0.350    0.734
## `corsi_corr_pre-degree`   0.8696     3.5433   0.245    0.811
## `kirk_acc_pre-degree`    26.7125    42.5877   0.627    0.545
## `mlat_acc_pre-degree`    10.5758    19.3488   0.547    0.597
## self_rating               2.3496     3.9823   0.590    0.568
## 
## Residual standard error: 14.14 on 10 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.106,  Adjusted R-squared:  -0.4304 
## F-statistic: 0.1976 on 6 and 10 DF,  p-value: 0.97
# final assessments in 3rd year
( bsl_grade_mdl_3 <- lm(`grade_bsl_2nd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year` +
                                  self_rating,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##               -66.559                144.644                  5.327  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                 3.468                  6.254                -44.658  
##           self_rating  
##                -1.849
summary(bsl_grade_mdl_3)
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      26      28 
##  0.9097 -0.3039  0.5936  4.0291 -3.2764  1.4271 -2.0943 -1.2848 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -66.559     71.719  -0.928    0.524
## `nback_comb_3rd year`  144.644    138.183   1.047    0.485
## `mr3d_sats_3rd year`     5.327      3.390   1.571    0.361
## `mr2d_sats_3rd year`     3.468      8.459   0.410    0.752
## `corsi_corr_3rd year`    6.254      2.395   2.611    0.233
## `dspan_corr_3rd year`  -44.658    101.170  -0.441    0.735
## self_rating             -1.849      3.165  -0.584    0.663
## 
## Residual standard error: 6.026 on 1 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.9238, Adjusted R-squared:  0.4669 
## F-statistic: 2.022 on 6 and 1 DF,  p-value: 0.4917

BSL-SRT

( bsl_srt_mdl <- lm(bsl_srt ~ nback_comb + 
                                  mr3d_sats + 
                                  corsi_corr +
                                  self_rating,
                                  #kirk_acc +
                                  #kbit_acc + 
                                  #dspan_corr,
                                  data = apt) ) 
## 
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr + 
##     self_rating, data = apt)
## 
## Coefficients:
## (Intercept)   nback_comb    mr3d_sats   corsi_corr  self_rating  
##  -46.179130    75.472442     1.883576     0.434593     0.007539
summary(bsl_srt_mdl)
## 
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr + 
##     self_rating, data = apt)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.772 -3.277  0.507  2.495  5.864 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -46.179130  30.657088  -1.506   0.1663  
## nback_comb   75.472442  38.054691   1.983   0.0786 .
## mr3d_sats     1.883576   1.289609   1.461   0.1781  
## corsi_corr    0.434593   1.153233   0.377   0.7150  
## self_rating   0.007539   1.147214   0.007   0.9949  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.226 on 9 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.4738, Adjusted R-squared:  0.2399 
## F-statistic: 2.026 on 4 and 9 DF,  p-value: 0.1742
# only being fit on 8 or 9 observations...

# wide version - pre-degree
( bsl_srt_mdl_2 <- lm(`bsl_srt_3rd year` ~ 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `kirk_acc_pre-degree` +
                                   self_rating, 
                                   data = apt_wide) ) 
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + 
##     `kirk_acc_pre-degree` + self_rating, data = apt_wide)
## 
## Coefficients:
##             (Intercept)   `mr3d_sats_pre-degree`  `corsi_corr_pre-degree`  
##                 -6.0187                   1.3889                   1.2925  
##   `kirk_acc_pre-degree`              self_rating  
##                 12.9361                  -0.5175
summary(bsl_srt_mdl_2)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + 
##     `kirk_acc_pre-degree` + self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      26      28 
##  0.4041 -2.8902 -2.4367  0.5007  1.5638  2.2072  3.0150 -2.9032  0.5392 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -6.0187    13.9944  -0.430    0.689
## `mr3d_sats_pre-degree`    1.3889     1.1608   1.196    0.298
## `corsi_corr_pre-degree`   1.2925     1.0540   1.226    0.287
## `kirk_acc_pre-degree`    12.9361    11.6096   1.114    0.328
## self_rating              -0.5175     1.3071  -0.396    0.712
## 
## Residual standard error: 3.156 on 4 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.6017, Adjusted R-squared:  0.2034 
## F-statistic: 1.511 on 4 and 4 DF,  p-value: 0.3496
# final assessments in 3rd year
( bsl_srt_mdl_3 <- lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                   self_rating,
                                  #`dspan_corr_3rd year`,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##             -52.80663               60.39579                0.09081  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`            self_rating  
##               3.62600                2.40842               -0.71733
summary(bsl_srt_mdl_3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
##  0.64511 -1.15277 -0.52073  1.10524  0.49085  0.18383  1.53751  0.08069 
##       28 
## -2.36974 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -52.80663   23.13791  -2.282   0.1067  
## `nback_comb_3rd year`  60.39579   30.86857   1.957   0.1453  
## `mr3d_sats_3rd year`    0.09081    1.08422   0.084   0.9385  
## `mr2d_sats_3rd year`    3.62600    1.84731   1.963   0.1444  
## `corsi_corr_3rd year`   2.40842    0.64080   3.758   0.0329 *
## self_rating            -0.71733    0.94355  -0.760   0.5024  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.958 on 3 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.885,  Adjusted R-squared:  0.6934 
## F-statistic: 4.618 on 5 and 3 DF,  p-value: 0.119

Interpreting Grades

( terp_grade_mdl <- lm(grade_terp ~ nback_spat + 
                                    mr3d_sats + 
                                    corsi_corr +
                                    kirk_acc + 
                                    dspan_corr+
                                    self_rating,
                                    data = apt) )
## 
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr + 
##     kirk_acc + dspan_corr + self_rating, data = apt)
## 
## Coefficients:
## (Intercept)   nback_spat    mr3d_sats   corsi_corr     kirk_acc   dspan_corr  
##    349.1002    -328.2567       6.7148      -7.2400      61.9243     -51.8807  
## self_rating  
##      0.4704
summary(terp_grade_mdl)
## 
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr + 
##     kirk_acc + dspan_corr + self_rating, data = apt)
## 
## Residuals:
##      66      69      70      73      75      77      78      80      81      82 
##  4.4869  0.1530 -5.3732  1.7185 -3.9075 -1.5299  0.4653 -1.6238  4.8893  1.1100 
##      86 
## -0.3886 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  349.1002    77.5511   4.502   0.0108 *
## nback_spat  -328.2567    78.3829  -4.188   0.0138 *
## mr3d_sats      6.7148     1.6238   4.135   0.0144 *
## corsi_corr    -7.2400     2.0463  -3.538   0.0241 *
## kirk_acc      61.9243    17.5319   3.532   0.0242 *
## dspan_corr   -51.8807    23.4962  -2.208   0.0918 .
## self_rating    0.4704     1.8686   0.252   0.8137  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.943 on 4 degrees of freedom
##   (109 observations deleted due to missingness)
## Multiple R-squared:  0.9066, Adjusted R-squared:  0.7666 
## F-statistic: 6.475 on 6 and 4 DF,  p-value: 0.04601
# but is only being fit on 10 observations.....

# wide version - pre-degree
( terp_grade_mdl_2 <- lm(`grade_terp_2nd year` ~ 
                                  `nback_comb_pre-degree` + 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `kirk_acc_pre-degree` + 
                                  `dspan_corr_pre-degree`,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `dspan_corr_pre-degree`, data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                 -17.067                   39.109                    1.923  
## `corsi_corr_pre-degree`    `kirk_acc_pre-degree`  `dspan_corr_pre-degree`  
##                   2.730                   39.300                   -2.027
summary(terp_grade_mdl_2)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `dspan_corr_pre-degree`, data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.5754  -1.6045   0.2377   3.1024  11.7750 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -17.067     47.712  -0.358    0.730
## `nback_comb_pre-degree`   39.109     58.510   0.668    0.523
## `mr3d_sats_pre-degree`     1.923      1.427   1.348    0.215
## `corsi_corr_pre-degree`    2.730      2.848   0.959    0.366
## `kirk_acc_pre-degree`     39.300     47.544   0.827    0.432
## `dspan_corr_pre-degree`   -2.027     42.627  -0.048    0.963
## 
## Residual standard error: 8.88 on 8 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.5471, Adjusted R-squared:  0.264 
## F-statistic: 1.933 on 5 and 8 DF,  p-value: 0.1943
# final assessments in 3rd year
( terp_grade_mdl_3 <- lm(`grade_terp_2nd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year` +
                                  self_rating,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` + 
##     `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` + 
##     `dspan_corr_3rd year` + self_rating, data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##              -13.8323                30.6269                 3.9546  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                9.0049                 4.5533                -0.1906  
##           self_rating  
##                1.4224
summary(terp_grade_mdl_3)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` + 
##     `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` + 
##     `dspan_corr_3rd year` + self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      26      28 
##  1.2483 -1.6037 -0.9845  0.9173 -1.1444  4.9873 -2.1785 -0.5573 -0.6845 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)           -13.8323    51.3678  -0.269    0.813
## `nback_comb_3rd year`  30.6269    81.6812   0.375    0.744
## `mr3d_sats_3rd year`    3.9546     2.4033   1.645    0.242
## `mr2d_sats_3rd year`    9.0049     4.1290   2.181    0.161
## `corsi_corr_3rd year`   4.5533     1.7225   2.643    0.118
## `dspan_corr_3rd year`  -0.1906    47.5514  -0.004    0.997
## self_rating             1.4224     2.1120   0.673    0.570
## 
## Residual standard error: 4.339 on 2 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.949,  Adjusted R-squared:  0.796 
## F-statistic: 6.203 on 6 and 2 DF,  p-value: 0.1453

BSL>Eng interpreting

( terp_b2e_mdl <- lm(terp_b2e ~ nback_spat + 
                                    mr3d_sats + 
                                    corsi_corr +
                                    dspan_corr +
                                    self_rating,
                                    data = apt) )
## 
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr + self_rating, data = apt)
## 
## Coefficients:
## (Intercept)   nback_spat    mr3d_sats   corsi_corr   dspan_corr  self_rating  
##      20.065       60.044        2.279        2.615      -27.812       -1.871
summary(terp_b2e_mdl)
## 
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr + self_rating, data = apt)
## 
## Residuals:
##    103    105    107    108    110    111    112    118 
## -3.714 -1.343  1.808  3.084 -3.811 -2.718  5.330  1.364 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   20.065     65.433   0.307    0.788
## nback_spat    60.044     89.747   0.669    0.572
## mr3d_sats      2.279      3.358   0.679    0.567
## corsi_corr     2.615      4.115   0.635    0.590
## dspan_corr   -27.812     72.266  -0.385    0.737
## self_rating   -1.871      3.927  -0.476    0.681
## 
## Residual standard error: 6.346 on 2 degrees of freedom
##   (112 observations deleted due to missingness)
## Multiple R-squared:  0.3849, Adjusted R-squared:  -1.153 
## F-statistic: 0.2503 on 5 and 2 DF,  p-value: 0.9081
# but is only being fit on 10 observations.....

# wide version - pre-degree
( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~ 
                                  `nback_comb_pre-degree` + 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `dspan_corr_pre-degree`+
                                  self_rating,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                 911.589                 -464.608                    1.394  
## `corsi_corr_pre-degree`  `dspan_corr_pre-degree`              self_rating  
##                  22.437                -1009.482                       NA
summary(terp_b2e_mdl_2)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
## ALL 5 residuals are 0: no residual degrees of freedom!
## 
## Coefficients: (1 not defined because of singularities)
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               911.589         NA      NA       NA
## `nback_comb_pre-degree`  -464.608         NA      NA       NA
## `mr3d_sats_pre-degree`      1.394         NA      NA       NA
## `corsi_corr_pre-degree`    22.437         NA      NA       NA
## `dspan_corr_pre-degree` -1009.482         NA      NA       NA
## self_rating                    NA         NA      NA       NA
## 
## Residual standard error: NaN on 0 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:      1,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 4 and 0 DF,  p-value: NA
( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~ 
                                  `mr3d_sats_pre-degree`+
                                  self_rating,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Coefficients:
##            (Intercept)  `mr3d_sats_pre-degree`             self_rating  
##                66.4684                  2.4042                  0.2235
# final assessments in 3rd year
( terp_b2e_mdl_3 <- lm(`terp_b2e_3rd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year`+
                                  self_rating,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##               44.1365                33.9364                -3.1414  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                8.3071                -0.1019                -8.7811  
##           self_rating  
##                2.6782
summary(terp_b2e_mdl_3)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
## -1.2286  1.8594  1.3950  0.1892  0.3327 -5.6259  2.6600  0.4183 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            44.1365    80.2710   0.550    0.680
## `nback_comb_3rd year`  33.9364   128.2479   0.265    0.835
## `mr3d_sats_3rd year`   -3.1414     6.5653  -0.478    0.716
## `mr2d_sats_3rd year`    8.3071     8.1347   1.021    0.493
## `corsi_corr_3rd year`  -0.1019     4.4937  -0.023    0.986
## `dspan_corr_3rd year`  -8.7811    80.2661  -0.109    0.931
## self_rating             2.6782     4.9123   0.545    0.682
## 
## Residual standard error: 6.779 on 1 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.649,  Adjusted R-squared:  -1.457 
## F-statistic: 0.3082 on 6 and 1 DF,  p-value: 0.8782

Eng>BSL interpreting

( terp_e2b_mdl <- lm(terp_e2b ~ nback_spat + 
                                    mr3d_sats + 
                                    corsi_corr +
                                    dspan_corr+
                                    self_rating,
                                    data = apt) )
## 
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr + self_rating, data = apt)
## 
## Coefficients:
## (Intercept)   nback_spat    mr3d_sats   corsi_corr   dspan_corr  self_rating  
##    -74.3498     124.0204       0.4579       4.0471      14.3122      -1.0351
summary(terp_e2b_mdl)
## 
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr + self_rating, data = apt)
## 
## Residuals:
##      103      105      107      108      110      111      112      116 
## -17.4064  -1.6379  -0.8478  -0.2062 -14.0278   0.3512  15.1243   7.4936 
##      118 
##  11.1570 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -74.3498   177.8746  -0.418    0.704
## nback_spat  124.0204   243.5084   0.509    0.646
## mr3d_sats     0.4579     6.6724   0.069    0.950
## corsi_corr    4.0471     6.6784   0.606    0.587
## dspan_corr   14.3122   168.1128   0.085    0.938
## self_rating  -1.0351     8.2803  -0.125    0.908
## 
## Residual standard error: 17.44 on 3 degrees of freedom
##   (111 observations deleted due to missingness)
## Multiple R-squared:  0.2709, Adjusted R-squared:  -0.9442 
## F-statistic: 0.223 on 5 and 3 DF,  p-value: 0.9301
# but is only being fit on 10 observations.....

# wide version - pre-degree
( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~ 
                                  `nback_comb_pre-degree` + 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `dspan_corr_pre-degree`+
                                  self_rating,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                -201.531                  244.387                    8.151  
## `corsi_corr_pre-degree`  `dspan_corr_pre-degree`              self_rating  
##                  17.899                  -17.186                  -18.894
summary(terp_e2b_mdl_2)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
## ALL 6 residuals are 0: no residual degrees of freedom!
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)             -201.531         NA      NA       NA
## `nback_comb_pre-degree`  244.387         NA      NA       NA
## `mr3d_sats_pre-degree`     8.151         NA      NA       NA
## `corsi_corr_pre-degree`   17.899         NA      NA       NA
## `dspan_corr_pre-degree`  -17.186         NA      NA       NA
## self_rating              -18.894         NA      NA       NA
## 
## Residual standard error: NaN on 0 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:      1,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 5 and 0 DF,  p-value: NA
( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~ 
                                  `mr3d_sats_pre-degree`+
                                  self_rating,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Coefficients:
##            (Intercept)  `mr3d_sats_pre-degree`             self_rating  
##                 62.273                   5.158                   2.062
# final assessments in 3rd year
( terp_e2b_mdl_3 <- lm(`terp_e2b_3rd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year`+
                                  self_rating,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##               -60.018                143.155                 -8.995  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                25.276                  6.510                -62.459  
##           self_rating  
##                 4.206
summary(terp_e2b_mdl_3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
## -1.73445  3.30150  2.99499  2.89611 -1.44091 -9.66953  4.99733 -1.32066 
##       28 
## -0.02438 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -60.018    103.742  -0.579   0.6214  
## `nback_comb_3rd year`  143.155    164.963   0.868   0.4770  
## `mr3d_sats_3rd year`    -8.995      4.854  -1.853   0.2050  
## `mr2d_sats_3rd year`    25.276      8.339   3.031   0.0938 .
## `corsi_corr_3rd year`    6.510      3.479   1.871   0.2022  
## `dspan_corr_3rd year`  -62.459     96.035  -0.650   0.5822  
## self_rating              4.206      4.265   0.986   0.4280  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.762 on 2 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.8773, Adjusted R-squared:  0.5093 
## F-statistic: 2.384 on 6 and 2 DF,  p-value: 0.3247